The present invention relates to cardiac imaging, and more particularly, to extracting centerlines of coronary arteries in 3D medical image data.
Cardiovascular disease (CVD) is the leading cause of death in the United States and coronary stenosis (i.e., narrowing of the vessel) is the most common CVD. Cardiac computed tomography (CT) is the primary non-invasive imaging modality used to diagnose coronary stenosis due to its superior image resolution. To facilitate diagnosis of coronary stenosis, coronary artery centerline extraction is a prerequisite for the subsequent quantification of the coronary stenosis in which a percentage of the lumen area blocked by plaques is measured. Various centerline extraction methods have been proposed. Most conventional centerline extraction methods are data-driven and attempt to trace a centerline from an automatically detected or manually specified coronary ostium. One prominent advantage of such approaches is the potential to handle anatomical variations of the coronary arteries. However, since no or little high-level prior information is used, such data-driven coronary artery extraction procedures are often pre-terminated at a severe occlusion or unusual centerline course may be generated. In clinical practice, it is desirable to assign a label (i.e., branch name) to each branch in the extracted coronary tree, or at least identify the four major arteries, i.e., the left main (LM) artery, the left anterior descending (LAD) artery, the left circumflex (LCX) artery, and the right coronary artery (RCA). However, labeling of the branches of the coronary artery tree is not an easy task if the coronary tree is not extracted completely or some branches are traced incorrectly into non-coronary structures. Such a two-step approach (coronary tree extraction followed by branch labeling) is not optimal. Each step is made more difficult due to the limited usage of high-level prior information. Accordingly, a coronary centerline extraction approach that improves the robustness of conventional data-driven approaches is desirable.